I agree that the example would be better if it raised questions about the weaknesses of this approach and the next steps you might need for a more general solut

I agree that the example would be better if it raised questions about the weaknesses of this approach and the next steps you might need for a more general solution. But it is still a legitimate example. I'm an amateur, but I'm pretty sure the world of production grade vision systems is full of hacks like this. A good example is a nose detector on a depth sensor: just look for the closest point to the camera. John Russ's Image Procesing Handbook (amazon.com/Image-Processing-Handbook-Sixth-Edition/dp/1439840458) is full of examples where simple techniques are good enough under controlled circumstances. True object recognition would require strong AI. Every real life vision system is a compromise that makes many assumptions about its input. You'd be foolish to reach for something like SIFT if you really know that the car is always yellow and it always parks in the same 200x200px square.